Skip to main content
SpringerLink
Log in

Supervised deep hashing for image content security

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Due to the fast growth of image data on the web, it is necessary to ensure the content security of uploaded images. One of the fundamental problems behind this need is retrieving relevant images from the large-scale databases. Recently, hashing/binary coding algorithms have proved to be effective for large-scale visual information retrieval. Most existing hashing methods usually seek single linear projections to map each sample into a binary vector. In this paper, a supervised deep hashing method is proposed, which seeks multiple non-linear transformations to generate more discriminative binary codes with short bits. We implement a deep Convolutional Neural Network to achieve end-to-end hashing. A loss function is elaborately devised to preserve the similarity relationship between images, meanwhile minimize the quantization error and make hash bits distribute evenly. Extensive experimental comparisons with state-of-the-art hashing algorithms are conducted on CIFAR-10 and NUS-WIDE, the MAP reaches to 87.67% and 77.48% with 48 bits respectively. It shows that the proposed method achieves very competitive results with the state-of-the-arts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Andoni A, Indyk P (2006) Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: 2006 47th annual IEEE symposium on foundations of computer science (FOCS’06). IEEE, pp 459–468

  2. Calonder M, Lepetit V, Strecha C, Fua P (2010) BRIEF: binary robust independent elementary features. In: European conference on computer vision, pp 778–792

  3. Chua TS, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) NUS-WIDE: a real-world web image database from National University of Singapore. In: Proceedings of the ACM international conference on image and video retrieval. ACM, p 48

  4. Chum O, Philbin J, Zisserman A et al (2008) Near duplicate image detection: min-hash and tf-idf weighting. In: BMVC, vol 810, pp 812–815

  5. Dai J, Li Y, He K, Sun J (2016) R-fcn: object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379–387

  6. Deng J, Ding N, Jia Y, Frome A, Murphy K, Bengio S, Li Y, Neven H, Adam H (2014) Large-scale object classification using label relation graphs. In: European Conference on Computer Vision. Springer, pp 48–64

  7. Gionis A, Indyk P, Motwani R et al (1999) Similarity search in high dimensions via hashing. In: VLDB, vol 99, pp 518–529

  8. Girshick R (2015) Fast R-CNN. In: IEEE international conference on computer vision, pp 1440–1448

  9. Gong Y, Lazebnik S (2011) Iterative quantization: a procrustean approach to learning binary codes. In: IEEE conference on computer vision and pattern recognition (CVPR), 2011. IEEE, pp 817–824

  10. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034

  11. Jain P, Kulis B, Grauman K (2008) Fast image search for learned metrics. In: IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE, pp 1–8

  12. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia. ACM, pp 675–678

  13. Jin Z, Li C, Lin Y, Cai D (2014) Density sensitive hashing. IEEE Transactions on Cybernetics 44(8):1362

    Article  Google Scholar 

  14. Kang WC, Li WJ, Zhou ZH (2016) Column sampling based discrete supervised hashing. In: Thirtieth AAAI conference on artificial intelligence

  15. Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Technical report, University of Toronto

  16. Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  17. Kulis B, Grauman K (2011) Kernelized locality-sensitive hashing. IEEE Trans Pattern Anal Mach Intell 34(6):1092

    Article  Google Scholar 

  18. Lai H, Pan Y, Liu Y, Yan S (2015) Simultaneous feature learning and hash coding with deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3270–3278

  19. Leutenegger S, Chli M, Siegwart RY (2011) BRISK: binary robust invariant scalable keypoints. In: International conference on computer vision, pp 2548–2555

  20. Li X, Lin G, Shen C, Hengel A, Dick A (2013) Learning hash functions using column generation. In: International Conference on Machine Learning, pp 142–150

  21. Lin G, Shen C, Shi Q, van den Hengel A, Suter D (2014) Fast supervised hashing with decision trees for high-dimensional data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1963–1970

  22. Liong VE, Lu J, Wang G, Moulin P (2015) Deep hashing for compact binary codes learning. In: IEEE conference on computer vision and pattern recognition, pp 2475–2483

  23. Liu H, Wang R, Shan S, Chen X (2016) Deep supervised hashing for fast image retrieval. In: IEEE conference on computer vision and pattern recognition

  24. Liu W, Wang J, Kumar S, Chang SF (2011) Hashing with graphs. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 1–8

  25. Liu W, Wang J, Ji R, Jiang YG, Chang SF (2012) Supervised hashing with kernels. In: IEEE conference on computer vision and pattern recognition (CVPR), 2012. IEEE, pp 2074–2081

  26. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision. Springer, pp 21–37

  27. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: IEEE conference on computer vision and pattern recognition, pp 3431–3440

  28. Lowe DG, Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91

    Article  MathSciNet  Google Scholar 

  29. Norouzi ME, Fleet DJ (2011) Minimal loss hashing for compact binary codes. In: International conference on machine learning, ICML 2011, Bellevue, Washington, USA, pp 353–360

  30. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145

    Article  Google Scholar 

  31. Raginsky M, Lazebnik S (2009) Locality-sensitive binary codes from shift-invariant kernels. In: Advances in neural information processing systems, pp 1509–1517

  32. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779–788

  33. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  34. Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: IEEE international conference on computer vision, pp 2564–2571

  35. Salakhutdinov R, Hinton G (2009) Semantic hashing. Int J Approx Reason 50(7):969

    Article  Google Scholar 

  36. Shen F, Shen C, Liu W, Tao Shen H (2015) Supervised discrete hashing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 37–45

  37. Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Advances in neural information processing systems, pp 1988–1996

  38. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  39. Vandergheynst P, Ortiz R, Alahi A (2012) FREAK: fast retina keypoint. In: Computer vision and pattern recognition, pp 510–517

  40. Wang J, Kumar S, Chang SF (2010) Sequential projection learning for hashing with compact codes. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 1127–1134

  41. Weiss Y, Torralba A, Fergus R (2009) Spectral hashing. In: Advances in neural information processing systems, pp 1753–1760

  42. Xia R, Pan Y, Lai H, Liu C, Yan S (2014) Supervised hashing for image retrieval via image representation learning. In: AAAI, vol 1, p 2

  43. Xie H, Gao K, Zhang Y, Li J, Liu Y, Ren H (2010) Effective and efficient image copy detection based on GPU. In: European Conference on Computer Vision. Springer, pp 338–349

  44. Xie H, Gao K, Zhang Y, Li J (2011) Local geometric consistency constraint for image retrieval. In: IEEE international conference on image processing, pp 101–104

  45. Xie H, Zhang Y, Gao K, Tang S, Xu K, Guo L, Li J (2013) Robust common visual pattern discovery using graph matching. J Vis Commun Image Represent 24(5):635

    Article  Google Scholar 

  46. Xie H, Zhang Y, Tan J, Guo L, Li J (2014) Contextual query expansion for image retrieval. IEEE Trans Multimedia 16(4):1104

    Article  Google Scholar 

  47. Zhang R, Lin L, Zhang R, Zuo W, Zhang L (2015) Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans Image Process 24(12):4766

    Article  MathSciNet  Google Scholar 

  48. Zhao F, Huang Y, Wang L, Tan T (2015) Deep semantic ranking based hashing for multi-label image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1556–1564

Download references

Acknowledgements

This work is supported by the National Nature Science Foundation of China (61771468), the Youth Innovation Promotion Association Chinese Academy of Sciences (2017209). Thanks to the contributions made by Yan Li from Beijing Kuaishou Technology Co., Ltd., who has examined the whole manuscript with respect to the usages of verb tense, singular and plural forms of nouns, and articles and has also conducted some experiments.

Author information

Author notes

    Authors and Affiliations

    1. School of Information and Electrical Engineering, Ludong University, Yantai, China

      Yanping Ma

    2. School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, Shandong, China

      Dongbao Yang & Jian Yin

    3. School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China

      Dongbao Yang & Hongtao Xie

    Authors
    1. Yanping Ma
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Dongbao Yang
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Hongtao Xie
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Jian Yin
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding authors

    Correspondence to Dongbao Yang or Hongtao Xie.

    Additional information

    Yanping Ma and Dongbao Yang contributed equally to this work and should be considered co-first authors.

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Ma, Y., Yang, D., Xie, H. et al. Supervised deep hashing for image content security. Multimed Tools Appl 78, 661–676 (2019). https://doi.org/10.1007/s11042-017-5433-z

    Download citation

    • Received:

    • Revised:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s11042-017-5433-z

    Keywords

    Search

    Navigation